tailieunhanh - báo cáo hóa học:" Research Article Performance Analysis of Bit-Width Reduced Floating-Point Arithmetic Units in FPGAs: A Case Study of Neural Network-Based Face Detector"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Performance Analysis of Bit-Width Reduced Floating-Point Arithmetic Units in FPGAs: A Case Study of Neural Network-Based Face Detector | Hindawi Publishing Corporation EURASIP Journal on Embedded Systems Volume 2009 Article ID 258921 11 pages doi 2009 258921 Research Article Performance Analysis of Bit-Width Reduced Floating-Point Arithmetic Units in FPGAs A Case Study of Neural Network-Based Face Detector Yongsoon Lee 1 Younhee Choi 1 Seok-Bum Ko 1 and Moon Ho Lee2 1 Electrical and Computer Engineering Department University of Saskatchewan Saskatoon SK Canada S7N 5A9 2 Institute of Information and Communication Chonbuk National University Jeonju South Korea Correspondence should be addressed to Seok-Bum Ko Received 4 July 2008 Revised 16 February 2009 Accepted 31 March 2009 Recommended by Miriam Leeser This paper implements a field programmable gate array- FPGA- based face detector using a neural network NN and the bitwidth reduced floating-point arithmetic unit FPU . The analytical error model using the maximum relative representation error MRRE and the average relative representation error ARRE is developed to obtain the maximum and average output errors for the bit-width reduced FPUs. After the development of the analytical error model the bit-width reduced FPUs and an NN are designed using MATLAB and VHDL. Finally the analytical MATLAB results along with the experimental VHDL results are compared. The analytical results and the experimental results show conformity of shape. We demonstrate that incremented reductions in the number of bits used can produce significant cost reductions including area speed and power. Copyright 2009 Yongsoon Lee et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Neural networks have been studied and applied in various fields requiring learning classification fault tolerance and associate memory since the 1950s. The neural networks are frequently used to model